Counting values

Count repeated values in a list, pandas.Series, or pandas.DataFrame. This function adds to the pandas value_counts method in a few ways. It counts values absolutely, and as a percentage. It provides cumulative counts and cumulative percentages. It also lumps all values beyond the show_top values under “Others:”. You alsoe get the values colored with the sequential color scale of your choice.

source

value_counts

 value_counts (data, dropna=False, show_top=10, sort_others=False,
               title=None, style=True, width=900, height=650,
               colorscale='cividis')

Count the values of data and return a table of counts (absolute, cumulative, percentage, and cumulative percentage).

	Type	Default	Details
data	list, tuple, pandas.Series, pandas.DataFrame		A collection of items to count, using any of the above data structures.
dropna	bool	False	Wether or not to drop missing values.
show_top	int	10	How many top items to show. All remaining items, will be grouped into “Others:”.
sort_others	bool	False	Whether or not to put “Others” in their sorted order. The default is to have this item at the bottom.
title	NoneType	None	The title of the chart.
style	bool	True	Whether or not to style the resulting table with a heatmap.
width	int	900	The width in pixels of the resulting figure. Set this to None to make it use the widthe of its container.
height	int	650	The width in pixels of the resulting figure. Set this to None to make it use the widthe of its container.
colorscale	str	cividis	Which color scale to use for the heatmaps.
Returns	plotly.graph_objects.Figure

Get URL data and count values

apple = pd.read_csv('data/apple_url_list.csv')
urldf = adv.url_to_df(apple['url'])
urldf.head()

	url	scheme	netloc	path	query	fragment	dir_1	dir_2	dir_3	dir_4	...	query_src	query_tab	query_modelList	query_itscg	query_itsct	query_select	query_useASL	query_int_cid	query_board_id	query_sel
0	https://www.apple.com/ae/shop/accessories/all	https	www.apple.com	/ae/shop/accessories/all	NaN	NaN	ae	shop	accessories	all	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1	https://www.apple.com/ae/shop/accessories/all/...	https	www.apple.com	/ae/shop/accessories/all/accessibility	NaN	NaN	ae	shop	accessories	all	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2	https://www.apple.com/ae/shop/accessories/all/...	https	www.apple.com	/ae/shop/accessories/all/airtag	NaN	NaN	ae	shop	accessories	all	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
3	https://www.apple.com/ae/shop/accessories/all/...	https	www.apple.com	/ae/shop/accessories/all/beats	NaN	NaN	ae	shop	accessories	all	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
4	https://www.apple.com/ae/shop/accessories/all/...	https	www.apple.com	/ae/shop/accessories/all/beats-featured	NaN	NaN	ae	shop	accessories	all	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

5 rows × 28 columns

Default behavior for a single column

value_counts(urldf['dir_1'])

Count unique row combinations in more than one column

Setting width=None will make the width responsive and takes on the width of the chart’s container.

value_counts(urldf[['dir_1', 'dir_2']], width=None)

Filter for a better overview and change `colorscale`

value_counts(urldf[urldf['dir_1'].eq('shop')]['dir_2'], colorscale='magma')

Filter data and set an informative title for the chart

value_counts(urldf[urldf['dir_1'].eq('ca')]['dir_2'], title='<b>apple.com/ca/dir_2/</b> directory counts')